Conv2D Parameter Calculator
Weights and biases of a conv layer — standard, depthwise and depthwise-separable compared side by side.
Depthwise-separable convs (MobileNet's core trick) typically need 10–12% of standard-conv parameters at 3×3 — the table updates live as you change channels.
Formula
About Conv2D Parameter Calculator
A convolution's parameter bill is k²·Cin·Cout — innocuous at the first layer, explosive by the last. This calculator prices any layer instantly and, crucially, compares the standard formulation against depthwise and depthwise-separable variants, the substitution that made MobileNet-class edge vision possible. At the default 128→256-channel 3×3, separable needs just 11% of the standard layer's weights. Use it when porting models to microcontrollers, sizing custom backbones, or explaining to a colleague why their 7×7 conv on 512 channels costs 12.8M parameters.
How to use Conv2D Parameter Calculator
- 1Enter your values into Conv2D Parameter Calculator — sensible, domain-typical defaults are pre-filled so you see a real result immediately.
- 2The result recomputes live using the formula shown on the page; there is no button to press.
- 3Adjust any input to compare scenarios, then read the worked example to see the substituted numbers.
Why use Conv2D Parameter Calculator?
- ✓Computes Conv2D Parameter instantly in your browser — no sign-up, no upload, no server round-trip.
- ✓100% free and unlimited, with the exact formula shown: standard: k².
- ✓Runs entirely client-side, so every value you enter stays private on your device.
- ✓Live recompute as you type, with a worked example and authoritative references for trust.
Frequently asked questions
Why does a depthwise-separable conv save so much?+
It factorizes: a k² depthwise conv filters each channel spatially (k²·Cin weights), then a 1×1 pointwise conv mixes channels (Cin·Cout). The expensive k²·Cin·Cout coupling never happens. Saving ≈ 1/Cout + 1/k² of the original — ~89% for 3×3 at 256 outputs.
Do separable convs lose accuracy?+
Slightly, per layer — but per parameter they win decisively, so at equal model size separable architectures (MobileNet, EfficientNet) dominate standard CNNs on edge budgets. At server scale the gap closes and regular convs/transformer blocks take over.
When does the bias matter?+
Numerically it adds only Cout parameters; practically it is dropped whenever BatchNorm follows the conv (BN's β subsumes it — keeping both wastes memory and breaks folding). Most modern backbones therefore use bias=False on convs feeding normalization.
How do grouped convolutions fit in?+
Groups g split channels into g independent convs: parameters become k²·(Cin/g)·(Cout/g)·g = standard/g. Depthwise is the extreme g=Cin case. ResNeXt's g=32 showed groups buy accuracy per parameter — this tool's depthwise option shows the far end of that spectrum.
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